Unsupervised topic identification by integrating linguistic and visual information based on hidden Markov models

Authors:
Tomohide Shibata;Sadao Kurohashi
Affiliations:
University of Tokyo, Tokyo, Japan;Kyoto University, Kyoto, Japan
Venue:
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Year:
2006

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Abstract

This paper presents an unsupervised topic identification method integrating linguistic and visual information based on Hidden Markov Models (HMMs). We employ HMMs for topic identification, wherein a state corresponds to a topic and various features including linguistic, visual and audio information are observed. Our experiments on two kinds of cooking TV programs show the effectiveness of our proposed method.